jgagneastro / coffeegrindsize Goto Github PK
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License: MIT License
Detects the individual coffee grounds in a white-background picture to determine particle size distribution
License: MIT License
Any version of the software can be run in Microsoft Windows?
The installation guide describes the "hard way" to get around the "unidentified developer" issue. It's easier to just Ctrl-click (or right-click) the app in Finder and select Open from the shortcut menu.
https://support.apple.com/guide/mac-help/open-a-mac-app-from-an-unidentified-developer-mh40616/mac
Obviously, this doesn't need to be fixed, but I just wanted to point it out. I am also a developer of an "evil" non-registered Mac app :-)
I found this project very useful, but hard to use for non-english speakers. So I made rough user guide for other Korean fellows.
간단하게 한국어 사용설명서를 만들었습니다.
It looks like we have a packaged distribution of this tool here. Can you please include the source files that produced the distribution? You mentioned that pull requests were welcome, and providing the source in that form would make it easier to do that.
While I think this is an awesome tool, the real value would be in gathering and aggregating as much of this sort of data together as a community. If we are able to share all our grind distributions from all of our own grinders and compare them to each other, then we would be able to use the data to answer some often asked questions such as:
Is grinder X comparable or better than grinder Y?
Is it true that beans from origin A produce more fines than those from origin B?
What effect does the roast level, days past roast, etc. have on the grind quality?
I created a sample repo with my own data, but it would be better if you can approve/administrate this project with your wider reach and influence.
https://github.com/Coffee-Grind-Distribution/coffee-grind-distribution
Running on Arch Linux, Particle Detection fails with the following error:
Exception in Tkinter callback
Traceback (most recent call last):
File "/usr/lib/python3.9/tkinter/__init__.py", line 1892, in __call__
return self.func(*args)
File "/home/user/coffeegrindsize/coffeegrindsize.py", line 592, in <lambda>
psd_button = Button(toolbar, text="Launch Particle Detection", command=lambda: self.launch_psd(None),highlightbackground=toolbar_bg)
File "/home/user/coffeegrindsize/coffeegrindsize.py", line 2341, in launch_psd
self.cluster_data.append(clusteri_data)
AttributeError: 'NoneType' object has no attribute 'append'
I'm not able to create a histogram after Particle Detection step:
Exception in Tkinter callback
Traceback (most recent call last):
File "/usr/lib/python3.9/tkinter/__init__.py", line 1892, in __call__
return self.func(*args)
File "/home/ghotrix/.local/lib/python3.9/site-packages/coffeegrindsize/scripts/coffeegrindsize.py", line 600, in <lambda>
histogram_button = Button(toolbar, text="Create Histogram", command=lambda: self.create_histogram(None), highlightbackground=toolbar_bg)
File "/home/ghotrix/.local/lib/python3.9/site-packages/coffeegrindsize/scripts/coffeegrindsize.py", line 2973, in create_histogram
figdata = figdata.reshape(fig.canvas.get_width_height()[::-1] + (3,))
ValueError: cannot reshape array of size 5298750 into shape (1130,1000,3)
The particle detection could be automated (and user to user bias removed) with an instance segmentation algorithm instead of thresholding. Given enough time and images, i can implement and train that if it's desired. I can fork off and start if 1) you think it's a good idea, and 2) there are a lot of images (hopefully with their threshold settings noted) somewhere or someway to get them. What do you think?
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